Systems and methods for image or video personalization with selectable effects

ABSTRACT

Embodiments relate to systems and methods for image or video personalization with selectable effects. Image data, which can include video sequences or digital still images, can be received in a graphical personalization tool to perform various image processing and related operations to insert personalized objects into the image data. In aspects, the personalized object(s) can be or include graphical inputs such as, for instance, textual information, graphical information, and/or other visual objects. The graphical personalization tool can automatically perform one or more processing stages in the image path, such as identifying key regions in a still image and/or key frames in a video sequence, in which personalized objects will be generated and inserted. Personalized objects can be extended to additional regions of a still image, can be animated across multiple still images, and/or can be extended to additional frames of a video sequence, all on an automated or user-assisted basis.

FIELD

The present teachings relate to systems and methods for image or videopersonalization with selectable effects, and more particularly, toplatforms and techniques for providing automated tools to assist a userin generating, editing, and inserting personalized graphical objectsinto digital still images or video sequences, by identifying key areasor key frames in the original image data and supporting the imageprocessing of the personalized objects for insertion.

BACKGROUND

In video, Web media, and other areas, the use of image processing and/orvideo production tools is known. In applications such as personal videoproduction, or others such as commercial advertising and otherproduction, the ability to generate and insert graphical objects into avideo sequence or graphical still image has been known. Suchapplications can be useful for producing specialized or personalizedadvertising content, such as video segments which incorporate the names,products or services of interest, and/or other information relevant toindividual users, or groups of users.

However, in known media production tools, the features and functionsavailable to produce personalized graphical objects can be cumbersomeand technical, and impose a significant amount of ramp-up time on thepart of the inexperienced user. Commercial-grade and similar video andgraphical tools can require the user to learn how to manually discover,locate, identify, and manipulate image source data. The user typicallythen must manually insert, adjust, format the personalized objects theywish to enter into a video sequence or still image, and makecorresponding compensations to the color, perspective, and otherattributes of an image or frame they wish to modify with personalizedcontent.

It is thus desirable to provide methods and systems for image or videopersonalization with selectable effects, in which the identification ofkey regions or frames of still images or video sequences can, ifdesired, be automatically performed for the user, and in which imageadjustment and other surrounding tasks can be automated and/or assistedvia an automated graphical personalization tool which does not requireextensive background knowledge or application training, while alsoproviding high-quality personalization effects.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the presentteachings and together with the description, serve to explain theprinciples of the present teachings. In the figures:

FIG. 1 illustrates an overall system configuration that can be used insystems and methods for image or video personalization with selectableeffects, according to various embodiments;

FIG. 2 illustrates a flowchart of processing that can be used in systemsand methods for image or video personalization with selectable effects,according to various embodiments;

FIG. 3 illustrates exemplary image processing operations and output,using video sequence data, according to various embodiments; and

FIG. 4 illustrates exemplary image processing operations and output,using digital still image data, according to various embodiments.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present teachings relate to systems and methods forimage or video personalization with selectable effects. Moreparticularly, embodiments relate to platforms and techniques foraccessing video or still image source data, analyzing the content ofthat image data, discovering key regions or key frames of that data forpotential personalization operations, and accepting user-inputtedgraphical objects to modify that image data to reflect content ofinterest to the user or groups of users. In aspects, the complement ofimage processing features and resources used to perform those and otheroperations can be integrated in a graphical personalization tool thatcan receive user-specified video sequences, animations, digital stillimages, and/or other image data, for instance, from personal computerstorage, cameras, online services, and/or other hosts or sources. Thegraphical personalization tool can be configured to locate, within theimage data, suitable frames or regions in which to insert of modifypersonalized graphical objects, such as personalized text, symbols,image inserts, and/or other objects selected and/or edited by the user.The graphical personalization tool can likewise perform automatedoperations on the one or more objects provided or edited by the user,such as, for instance, to alter the perspective on the personalizedobjects or image data on the whole, change the size, font, colorcharacteristics, and/or other attributes of the personalized object ordata in order to produce a realistic effect, and prepare the image datacontaining those inserted objects for printing, display, and/or otheroutput.

Reference will now be made in detail to exemplary embodiments of thepresent teachings, which are illustrated in the accompanying drawings.Where possible the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

FIG. 1 illustrates an overall system configuration in which systems andmethods for image or video personalization with selectable effects canoperate, according to aspects. In aspects as shown, an image source 102can produce, transmit, encode, and/or store image data 110, whereinimage data refers to still images or video sequences. In aspects, theimage source 102 can be or include, for instance, an imaging deviceand/or store such as a digital camera, smartphone camera, video camera,a scanner, a computer such as a desktop, laptop, and/or server computer,and/or other device, platform, service, and/or hardware. In aspects, theimage source 102 can generate, host, store, and/or maintain the imagedata 110 in local or remote storage, such as hard disk storage, opticalstorage, electronic memory, and/or other types of storage hardware ormedia. According to aspects, the image data 110 can be an image fileencoded, for example, in a comparatively high-resolution and/orhigh-color palette (e.g., 24- or 32-bit) format. The image data 110 canbe encoded, merely for example, in an RGB color space, or CIELAB(luminance-chrominance) format or color space specified in theInternational Commission on Illumination (CIE) 1976 color spacestandard. In aspects, the image data 110 can be encoded in other formatsor according to other standards, such as the TIFF (Tagged Image FileFormat), the RAW file format, JPEG format, MP4 format, and/or others.According to aspects, as noted the image source 102 can be or include adevice which captures and initially encodes or stores the image data110, but can also be or include a device or service which receives andstores the image data 110 from one or more other sources, such as, forinstance, an online digital video and/or photo storage site or service.

In aspects, the image source 102 can communicate with a graphicalpersonalization tool 104 to receive, analyze, manipulate, and/orpersonalize the image data 110 to generate personalized image/videooutput 106, which can be or include the original image data tailored toinclude specialized or personalized textual, graphical, and/or othercontent. In aspects, the graphical personalization tool 104 can be orinclude computer and/or other hardware resources such as a desktop,laptop, server, and/or other computer, and/or a smartphone or othernetworked digital device. In aspects, the graphical personalization tool104 can also or instead be or include software and/or service resources,such as applications, operating systems, online portals or services,and/or other software-based logic or services.

According to aspects, a user can operate the graphical personalizationtool 104, locally or remotely, to generate, edit, and/or insert a set ofpersonalized objects 108 into the image data 110, and thereby create aspecialized, personalized, and/or otherwise customized version of theimage data 110 represented by the personalized output 106. In aspects,and merely for example, the personalized output 106 can include contentsuch as personal names, business names, the logos of preferred productsor services, advertisements or other promotions, indicators ofgeographic location, and/or other types or classes of content orinformation that can serve to render the image data 110 morepersonalized or relevant to a specific user, and/or groups of users.Moreover, and as described herein, the graphical personalization tool104 can automatically perform a variety of image processing tasks toaccomplish the insertion of the personalized objects 108 into the output106, and/or can support and assist the user of the graphicalpersonalization tool 104 in doing so.

More specifically and as for instance illustrated in the flowchart ofFIG. 2, the graphical personalization tool 104 can be operated toperform a set of tasks on the image data 110 to manipulate personalizedcontent, and eventually generate the personalized output 106. In aspectsas shown, processing can begin in 202. In 204, the graphicalpersonalization tool 104 can select or identify one or more keys framesin the image data 110. In implementations, the graphical personalizationtool 104 can select or identify one or more keys frames, on an automatedbasis. In aspects, one or more key frames can be identified, found,analyzed, or selected based on detected features of the frame, such aslarge smooth regions, regions of a specific texture, linear edges,planar surfaces, existing text, specific motion characteristics such asglobal linear motion, and/or others. In implementations, the graphicalpersonalization tool 104 can select or identify one or more key framesin the image 110 based on user input or selection, such as user reviewand input of a selected frame or frames to be used as key frame(s),and/or as a template or exemplar for additional key frames. In aspects,the user can select and/or input one or more objects in the set ofpersonalized objects 108 in the select key frame or frames. In 208, thegraphical personalization tool 104 can interpolate motions in remainingframes other than the selected key frames. In aspects, for instance, ifa user has selected, generated, and/or inserted a selection of text tobe placed on a wall in a room or on the side of a vehicle, that text canbe made to move and/or change position or perspective in additionalframes of the image data 110.

In 210, the graphical personalization tool 104 can query the user toidentify any errors in the appearance of the personalized objects 108 asinterpolated and/or inserted into additional frames or sections of theimage data 110. If the user responds by indicating that an error orinaccuracy exists in the set of personalized objects 108 inserted in theimage data 110, he/she is given the ability to correct the objectappearance within the tool, and processing can return to 208, wheremotion vectors and/or other features are interpolated to producerealistic object appearance in remaining frames of the image data 110.In 210, if the user does not identify or indicate any errors in the setof personalized objects 108 inserted in the image data 110, processingcan proceed to 214, and end. At or after 214, processing can also orinstead repeat, return to a prior processing point, or jump to a furtherprocessing point, besides terminating.

In terms of carrying out operations on the image data 110 when that datais, or includes, video frames or sequences, a number of techniques canbe used to generate and manage the set of personalized objects 108 forthat application, while exploiting features of video data, includingtemporal redundancy, that can provide increased efficiencies in imageprocessing of that type of content. In general, and as for instanceshown in FIG. 3, an original frame in the image data 110 can bediscovered, located, identified, and/or selected as a key frame forpersonalization operations. That frame can be presented to the user withoriginal and/or updated personalized information as part of a set ofpersonalized objects 108, illustratively shown as “My Text” located onthe side of a moving vehicle. The set of personalized objects 108reflected in that key frame can be presented to the user via thegraphical personalization tool 104, for the user to view and provide anycorrective inputs, if they wish. After receiving any corrections, edits,and/or updates from the user, the graphical personalization tool 104 canpropagate the updated or correct set of personalized objects 108 toframes located before the selected key frame and/or after the key frame,to generate a set of updated image data 110 that incorporates the set ofpersonalized objects 108 in all appropriate frames. In illustrativeexamples as shown, that propagated set of objects can include aninserted “My Text” message in frames containing the same or similarvehicle, setting, and/or object as the key frame. In aspects, theinserted “My Text” content can be inserted with correspondingadjustments to perspective, size, font, color, and/or other features toreflect the motion and/or animation being conveyed in the videosequence. If for example in the video sequence in FIG. 3 the truck ismoving along the road, then the “My Text” object will automatically movewith the truck, producing a natural appearance.

In terms of discovering, locating, identifying, and/or selecting a keyframe or frames in which the set of personalized objects 108 can beinserted and from there propagated to other frames, as noted more thanone technique can be used. In aspects, it may be noted that not allframes of a video sequence as part of the image data 110 need beanalyzed for purposes of incorporating personalized content. In aspects,one or more key frames can be identified that are particularly strong orsuitable candidates for personalization, and which represent one or avariety of contextual scenes. The remainder of the frames of the videosequence, and/or subsets of the remainder, can then be personalizedbased on the key frame or frames, using techniques described herein.

Discovering, identifying, and/or selecting the key frame or framesthemselves can involve analyzing the frames and/or scenes, anddetermining regions of interests. In implementations, the regions ofinterest can be or include surfaces in the foreground that can capture aviewer's attention. In aspects, planar surfaces can be used, but it willbe appreciated that other surfaces can be used, including cylindricalsurfaces (such as, e.g., bottles, cans, and/or other objects having acylindrical and/or concave surface). In aspects, incorporation of theset of personalized objects 108 can include the addition of new textualand/or other content onto a plain surface, and/or replacement ofexisting content on the surface, such as replacing the signage on amoving truck or other surface.

In implementations as noted, one technique to achieve or assist in keyframe identification is to prompt the user to make the selection and/oridentification. In various regards, the use of user-selected input forkey frame identification can be highly effective, since the user islikely to know or have a sense where the user wishes to place thepersonalized text and/or other personalized content. In cases, if thevideo data as part or all of the image data 110 is originally capturedwith the intent of later incorporating personalized content, the keyframe or frames may already be pre-identified. Once regions of interestare identified, the corresponding frames can in aspects be marked up askey frames.

In implementations, a selection approach that can be used in addition orinstead of user selection is to configure the selection based on theMotion Pictures Experts Group (MPEG) format, and/or metadata surroundthe MPEG data, when the image data 110 is, or contains, informationencoded in MPEG format. In aspects, frames are designated as “I” framesin MPEG format. The designated “I” frames in an MPEG video sequence canserve as key frames, in part because the “I” frames are independentlycompressed frames, and in addition, usually are used to indicated thestart of a new scene in the video sequence. While the “I” frames arenoted, it will be appreciated that other MPEG-based frames, and/or otherframes having desirable attributes for use as key frames, can be used.

In implementations, a further selection approach that can be used inaddition to, or instead of, the foregoing is to automatically analyzethe video frames contained in the image data 110, and compute apredefined metric or measure which can be used as part of the key frameselection criteria. In aspects, such a metric can be or include asuitability for personalization (SFP) metric, as described in U.S.patent application Ser. No. 13/349,751, filed Jan. 13, 2012, entitled“METHODS AND SYSTEM FOR ANALYZING AND RATING IMAGES FORPERSONALIZATION”, by Bala et al. (hereinafter “U.S. application No. Ser.13/349,751”), which is incorporated herein by reference in its entirety.As noted in U.S. application Ser. No. 13/349,751, the SFP metricanalyzes the image data 110, identifies all regions that are spatiallysmooth or containing existing text as candidate regions suitable oreligible for personalization, and subsequently derives a scoreindicating how suitable the frame and/or image is for personalization.Those frames with high SFP metric values or scores can be selected askey frames. The SFP metric can be adapted to search for dominant text ina scene, for example based on character size, motion trajectory, and/orother attributes. The SFP metric can likewise or instead be adapted tosearch for a specific text string in the video data, to besystematically replaced by a personalized message. Due to computationalloads that can be associated with the SFP metric, a subset of keyframes, such as the “I” frames in MPEG video data and relatively smallnumbers of frames in between, can be selected for analysis by the SFPmetric.

According to further implementations, since each key frame from a set ofvideo data contained in or comprising the image data 110 is itself astill image, approaches that are directed to still image personalizationcan also or instead be used for those selected frames. Still imagepersonalization techniques, for instance, described in U.S. patentapplication Ser. No. 12/340,103, filed Dec. 19, 2008, entitled “SYSTEMSAND METHODS FOR TEXT-BASED PERSONALIZATION OF IMAGES”, by Bala et al.,now US Publication No. 2010/0156919, published Jun. 24, 2010(hereinafter “US Publication No. 2010/0156919”) and U.S. patentapplication Ser. No. 12/964,820, filed Dec. 10, 2010, entitled“RENDERING PERSONALIZED TEXT ON CURVED IMAGE SURFACES”, by Bala et al.,now US Publication No. 2012/0146991, published Jun. 14, 2012(hereinafter US Publication No. 2012/0146991”), which are incorporatedherein in their entirety, can be used for those purposes. In approachesof those types, the graphical personalization tool 104 can present a keyframe to the user through a graphical user interface (GUI), andinterrogate the user to select a region to insert and/or replace textwithin the selected frame. As an alternative or complementary step, thegraphical personalization tool 104 can automatically suggest suitable orcandidate inclusion and/or exclusion regions for text insertion orreplacement, using known object recognition and/or other techniques. Thegraphical personalization tool 104 can then detect features such asstraight lines, elliptical curves, and/or others around the boundary ofthe identified region, followed by an initial estimation of the surfacegeometry, which can be computed and presented to the user. In aspects,the user can then make iterative adjustments, edits, and/or correctionsto the key frame, region, and/or inserted content, as part of the set ofpersonalized objects 108. The text and/or other content that isultimately selected can then be rendered, for instance, inthree-dimensional (3D) format using estimated surface geometry.

In addition, in implementations the personalization of key frames invideo data can be assisted by analyzing the following and/or adjacentframes. If the key frame in the image data 110 is blurred due tohandshake during video camera capture, fast motion, and/or other causes,the feature detection carried out in the key frame may be of poorquality. In these regards, it may be useful to select a subsequent videoframe which exhibits greater sharpness as the key frame, yielding betterfeature detection results. The detected features in the subsequent framecan then be back-tracked to the initial key frame, using a featurematching algorithm and/or other techniques.

According to aspects, once all selected key frames have beenpersonalized as described above, the remaining frames in the image data110 can be personalized efficiently using a motion vector analysis inthe vicinity of the selected region to approximate the motion of the setof personalized objects 108, including any text, in subsequent frames.To increase the accuracy of motion-vector based motion approximation, afeature detection and tracking algorithm can be computed around theregion in subsequent frames. This can serve as a verification step andcorrect any mistakes resulting from inaccurate estimates of motionvectors.

According to implementation, in addition to or instead of featuredetection algorithms noted above to correct motion and/or perspectivethe graphical personalization tool 104 can request the user to makecorrections in a “proofing” mode. In aspects employing this approach,the user can be presented with a graphical interface where the user canview the personalized video reflected in the personalized output 106,and mark or identify those frames in which the appearance of the set ofpersonalized objects 108 is noticeably incorrect or inaccurate. Inaspects, the corrective processing can be seen in FIG. 3, in which theuser can identify an error in perspective in the frame shown on the left(labeled “original frame”). The user can then be given a set ofinterface options to perform a correction. The interface options caninclude, for instance, an ability to drag the four corners of the text,to give the text the correct perspective on a planar surface, as shownin that figure. After processing the corrected perspective, thegraphical personalization tool 104 can produced the corrected frameincluding the set of personalized objects 108, as shown in the frame onthe right (labeled “user-corrected frame”). In aspects, an interpolationalgorithm can then be invoked or initiated to propagate the correctioninto adjacent frames.

According to aspects, it may be noted that systems and methods for imageor video personalization with selectable effects, includingpersonalization of video-based content as illustrated in FIGS. 2 and 3,can find application in various video fields, including commercialmarketing and political campaigns, but can be used in other applicationsas well. The inventive platforms and techniques can be used, forexample, in personalized medial video applications, where anindividual's video scan (e.g., Red Green Blue (RGB), ultrasound,magnetic resonance imaging (MRI), etc.) can be used as the backgroundand/or a template video. In such applications, the set of personalizedobjects 108 such as text, graphics, and/or images pertaining to a bodypart, function, and/or condition for a specific patient can beincorporated in a relatively seamless manner into the video content.Other applications are possible.

According to aspects, besides being configured to operate on video datato insert personalized content, the graphical personalization tool 104can also or instead be configured to process still image data, togenerate an animated and/or otherwise modified still image sequencewhich presents the set of personalized objects 108, with a motion-likeeffect. As for instance shown in FIGS. 4A and 4B, the user can select oraccess a still image as the source image data 110. In implementations insuch regards, the user can operate the graphical personalization tool104 to insert a set of personalized objects 108 into the still image,and present the still image as a constant or fixed background while theset of personalized objects 108 can be made to exhibit motion, viaanimation effects such as text moving on a road or wall. These types ofanimation effects are illustrated in FIGS. 4A and 4B, in which a user'sinserted text (“Welcome to Cornell” in FIG. 4A and “Try it out Raja!” inFIG. 4B), is generated and presented on a flat or cylindrical surfacerespectively, and then shown as moving along that surface, as indicatedby the arrow in each figure. In aspects, the text can be caused to notonly move on the surface, but also to perform other transformations,such as to change its size, rotate on the surface, change colors, and/orother effects, simultaneously with the text motion. In implementations,techniques described in the aforementioned US Publication No.2010/0156919 and US Publication No. 2012/0146991 can be used in textrendering operations, in which a three-dimensional (3D) pinhole cameramodel can allow the user to move the text freely around the planar,cylindrical, and/or other surface. It will be appreciated that othertext rendering techniques can be used. The user can thus specify amotion path for the set of personalized objects 108 including textinserts, and have that text and/or other objects recorded in ananimation layer incorporated in the personalized output 106. It may benoted that as long as the three-dimensional geometry and renderingparameters can be estimated from the image data 110, any type of complexmotion along the given surface can be specified. It may be noted thatthe personalized output 106 generated in this fashion can be encoded,recorded, and/or supported by or in standard formats such as AdobeFlash™ animations and/or animated GIF (graphical interchange format)data or files, or others. Such personalized output 106 can be useful inapplications such as, merely by example, electronic greeting cards,electronic advertisements, medical videos, and others.

According to aspects, various implementations described above related topersonalizing video content and personalizing still image content can becombined. For instance, in implementations the graphical personalizationtool 104 can be configured to incorporated personalized movies and/oranimations within an existing video. To perform such personalization,the perspective geometry and motion of the region within the templatevideo must be correctly estimated and tracked over time, to produce thepersonalized output 106. In further implementations, the concept ofchroma-keying can be used, in which a scene being captured is purposelydesigned so that the region and/or object to be personalized exhibits aknown fixed property, such as color and/or texture. This can cause theregion-identification and tracking operations to become easier or moreconvenient, and can also permit layering and occlusion effects to beincorporated. Examples of the latter include, for instance, thedepiction of people walking in front of a moving truck with apersonalized image or sign.

Various hardware, software, and other resources can be used inimplementations of image or video personalization with selectableeffects, according to embodiments. In embodiments, the graphicalpersonalization tool 104 can comprise a platform including processorcommunicating with memory, such as electronic random access memory,operating under control of or in conjunction with an operating system.The processor in embodiments can be incorporated in one or more servers,clusters, and/or other computers or hardware resources, and/or can beimplemented using cloud-based resources. The operating system can be,for example, a distribution of the Linux™ operating system, the Unix™operating system, or other open-source or proprietary operating systemor platform. The processor can communicate with data storage, such as adatabase stored on a local hard drive or drive array, to access or theimage data 110 and/or other content, media, or other data. The processorcan in implementations further communicate with a network interface,such as an Ethernet or wireless data connection, which in turncommunicates with one or more networks, such as the Internet or otherpublic or private networks. In implementations, the image data 110and/or other data can be received and/or accessed by the graphicalpersonalization tool 104 via the noted one or more networks. Theprocessor can, in general, be programmed or configured to executecontrol logic and control language processing operations, including toaccess, retrieve, manipulate, edit, and store the image data 110, theset of personalized objects 108, and/or the personalized output 106,among other data or outputs. Other configurations of the graphicalpersonalization tool 104, associated network connections, and otherhardware, software, and service resources are possible.

The foregoing description is illustrative, and variations inconfiguration and implementation may occur to persons skilled in theart. For example, while embodiments have been illustrated or describedin which the graphical personalization tool 104 is implemented orincorporated in one hardware and/or software module or resource, inimplementations, the graphical personalization tool 104 can beimplemented or incorporated across or in multiple hardware and/orsoftware modules or resources, such as for example in a cloudarchitecture. Those hardware and/or software modules or resources can belocal or distributed. Similarly, while embodiments have been illustratedor described in which one or more of the set of personalized objects 108are inserted in one key frame or key area of the image data 110, inaspects, the set of personalized objects 108, and/or multiple sets ofpersonalized objects 106, can be inserted or incorporated in multipleareas of one image or video frame, and/or in multiple images or videoframes. Other resources described as singular or integrated can inembodiments be plural or distributed, and resources described asmultiple or distributed can in embodiments be combined. The scope of thepresent teachings is accordingly intended to be limited only by thefollowing claims.

What is claimed is:
 1. A method of producing personalized image data forrendering on a display device, comprising: accessing image data from animage source; automatically identifying at least one of a key region ofthe image data, or a key frame of the image data, based on a set ofattributes of the image data; receiving, via user input, at least onepersonalized object for insertion into the image data; and inserting theat least one personalized object into the image data to producepersonalized output image data wherein at least one of the originalimage data and the personalized object exhibits motion.
 2. The method ofclaim 1, wherein the image data comprises at least one of a digitalstill image or digital video.
 3. The method of claim 1, wherein the atleast one personalized object comprises at least one of image, graphics,text, or video.
 4. The method of claim 1, wherein the set of imageattributes comprises at least one of user-identified attributes or a setof automatically-identified attributes.
 5. The method of claim 4,wherein the set of image attributes comprises a set ofautomatically-identified attributes based on at least one of textdetection operations, edge detection operations, smooth region findingoperations, texture analysis operations, or motion detection operations.6. The method of claim 1, wherein receiving the at least onepersonalized object comprises performing an image adjustment operationon the at least one personalized object to produce a desired realisticeffect.
 7. The method of claim 6, wherein the image adjustment operationcomprises at least one of performing a perspective adjustment, acontrast adjustment, or a sizing adjustment of the at least onepersonalized object.
 8. The method of claim 1, wherein the image datacomprises a video sequence, and further comprising extending theincorporation of at least one personalized object to additional framesof the image data.
 9. The method of claim 8, further comprisinginterpolating motion vectors to determine the location and appearance ofthe personalized object for the additional frames.
 10. The method ofclaim 1, wherein receiving the personalized object comprises receivinguser correction input to adjust the at least one personalized object.11. The method of claim 9, wherein the adjustment is made to at leastone of perspective, location, size, contrast, or color of thepersonalized object.
 12. The method of claim 1, further comprisingreceiving a set of exemplar personalized objects to present to the userfor selecting the at least one personalized object.
 13. The method ofclaim 2, wherein the image data comprises a digital still image, themethod further comprising specifying a direction and motion path of theat least one personalized object within the digital still image.
 14. Themethod of claim 11, further comprising animating the digital still imageusing the direction and motion path.
 15. The method of claim 1 where-inthe display device comprises at least one of a desktop computer, laptopcomputer, smartphone, or tablet.
 16. A system, comprising: an interfaceto an image source providing image data; and a processor, communicatingwith the image source via the interface, the processor being configuredto access the image data from the image source, automatically identifyat least one of a key region of the image data, or a key frame of theimage data, based on a set of attributes of the image data, receive, viauser input, at least one personalized object for insertion into theimage data, and insert the at least one personalized object into theimage data to produce personalized output image data wherein at leastone of the original image data and the personalized object exhibitsmotion.
 17. The system of claim 16, wherein the image data comprises atleast one of a digital still image or digital video.
 18. The system ofclaim 16, wherein the at least one personalized object comprises atleast one of graphics, text, or video.
 19. The system of claim 16,wherein the set of image attributes comprises at least one ofuser-identified attributes or a set of automatically-identifiedattributes.
 20. The system of claim 19, wherein the set of imageattributes comprises a set of automatically-identified attributes basedon at least one of text detection operations, edge detection operations,smooth region finding operations, texture analysis operations, or motiondetection operations.
 21. The system of claim 16, wherein receiving theat least one personalized object comprises performing an imageadjustment operation on the at least one personalized object to producea desired realistic effect.
 22. The system of claim 21, wherein theimage adjustment operation comprises at least one of performing aperspective adjustment, a contrast adjustment, or a sizing adjustment ofthe at least one personalized object.
 23. The system of claim 16,wherein the image data comprises a video sequence, and the processor isfurther configured to incorporate at least one personalized object toadditional frames of the image data.
 24. The system of claim 23, whereinthe processor is further configured to interpolate motion vectors todetermine the location and appearance of the personalized object for theadditional frames.
 25. The system of claim 16, wherein receiving thepersonalized object comprises receiving user correction input to adjustthe at least one personalized object.
 26. The system of claim 25,wherein the adjustment is made to at least one of perspective, location,size, contrast, or color of the personalized object.
 27. The system ofclaim 16, wherein the processor is further configured to receive a setof exemplar personalized objects to present for user selection as the atleast one personalized object.
 28. The system of claim 16, wherein theimage data comprises a digital still image, the processor being furtherconfigured to specify a direction and motion path of the at least onepersonalized object.
 29. The system of claim 28, wherein the processoris further configured to animate the digital still image using thedirection and motion path.
 30. The system of claim 16, furthercomprising a display device for display of the personalized output imagedata.
 31. The system of claim 30, wherein the display device comprisesat least one of a desktop computer, laptop computer, smartphone, ortablet.